git-shogg

###############################################################################
#                          1. Importing Libraries                             #
###############################################################################
import json
import datetime
import pandas as pd
from geopy import distance

git-shogg

###############################################################################
#                          2. User Inputs                                     #
###############################################################################
start_year, start_month, start_day = 2021, 6, 30
end_year, end_month, end_day = 2021, 7, 25
point_of_interest = (-77.85, 166.64)  # Latitude, Longitude
distance_from_point_of_interest = 0.1
aggregate_by = 'Day'   # This can be 'Year', 'Month', 'Day', 'Hour', 'Second'
weekday_exceptions = ['Saturday','Sunday'] # A list of weekdays that should excluded.

git-shogg

###############################################################################
#                          3. Get Data                                        #
###############################################################################
json_file = "Location History.json"
data = json.load(open(json_file))   # Read the file
locations = data['locations']

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###############################################################################
#                          4. Unpack the Data                                 #
###############################################################################
df_rows = []
for key in locations:
    timestamp = float(key['timestampMs'])
    latitude = float(key['latitudeE7'])/10000000
    longitude = float(key['longitudeE7'])/10000000
    
    if timestamp >= start_datetime and timestamp <= end_datetime:

git-shogg

###############################################################################
#                          5. Setup Useful Columns                            #
###############################################################################
# Determine the distance of each GPS coordinate from your point of interest.
distances_list = []
for lat, long in zip(df['latitude'],df['longitude']):
    distances_list.append(distance.geodesic(point_of_interest,(lat,long)).km)
df['distance'] = distances_list
df = df[df['distance'] < distance_from_point_of_interest]

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###############################################################################
#          6. Remove Weekday Exceptions and Aggregate the Results             #
###############################################################################
# Remove Weekday Exceptions
if weekday_exceptions != None:
    df = df[~df.weekday.isin(weekday_exceptions)]

# Aggregate the Results
df = df.groupby(aggregate_list).agg({'timestamp':['count'],'distance':['mean']})

git-shogg

#--- Import the Required Libraries ---
import requests
import json
import pandas as pd
import open_figi_key
import time
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

git-shogg

[{"name":"PAULSON & CO. INC.", "cik":"0001035674"}, 
{"name":"BAUPOST GROUP LLC", "cik":"0001061768"}, 
{"name":"BERKSHIRE HATHAWAY INC", "cik":"0001067983"}, 
{"name":"GREENLIGHT CAPITAL INC", "cik":"0001079114"}, 
{"name":"LONE PINE CAPITAL LLC", "cik":"0001061165"}, 
{"name":"SOROS FUND MANAGEMENT LLC", "cik":"0001029160"}, 
{"name":"APPALOOSA LP", "cik":"0001656456"}, 
{"name":"GOLDMAN SACHS GROUP INC", "cik":"0000886982"},
{"name":"SCION ASSET MANAGEMENT, LLC", "cik":"0001649339"}, 
{"name":"BRIDGEWATER ASSOCIATES, LP", "cik":"0001350694"}, 

git-shogg

# --- Function for running quick statistics on the dataframe ---
def dataframe_statistics(df, manager_name:str, cik:str):
    '''
    Build out the holdings dataframes with some basic statistics. 
    '''
    df['Portfolio percentage'] = (df['Holding value'] / df['Holding value'].sum()) * 100
    df['Manager Name'] = manager_name
    df['CIK'] = cik
    return df

git-shogg

# --- Functions for mapping CUSIPs to Tickers ---
def write_cusips_to_txt(cusip_mappings, cusip_mapping_file = 'cusips.txt'):
    '''
    Write cusip mappings to txt. Minimizing the need for API calls. 
    '''
    with open(cusip_mapping_file, 'w') as f:
        f.write(str(cusip_mappings))
    return

def read_cusips_from_txt(cusip_mapping_file='cusips.txt'):